This application claims Paris Convention priority of DE 10 2007 015 040.9 filed on Mar. 29, 2007.
The invention relates to a method for time-resolved imaging of N-dimensional magnetic resonance (=MR) with the following steps: Acquisition of MR signals from a sample volume by parallel imaging using multiple receiver coils, wherein a number of NtN-dimensional data matrices in k-space is acquired undersampled with reduction factor R from each receiver coil, wherein ky is the phase-encoding direction, and wherein the acquisition of the MR signals is performed according to an acquisition scheme that is periodic over time and describes the time sequence of the undersampled data matrices and reconstruction of missing data points of the acquisition scheme using a set of coil weighting factors and using N+1-dimensional reconstruction kernels that define from which acquired data points reconstruction will be performed.
Such a method is known from [7].
Parallel Imaging
Parallel imaging in MRT was first presented in 1997 [1] and is based on the use of coil arrays (multiple coil elements for simultaneous data acquisition), whose spatial variation in sensitivity is used for additional spatial encoding of the MR signal. Parallel imaging permits reduction of the acquisition time without any loss of spatial or temporal resolution of the data. Reduction of data acquisition entails undersampling of a data matrix. This data matrix comprises all points in the k-space of the volume under examination to be imaged at a certain measurement instant (data points), i.e. not all data points are measured during parallel imaging. This results in spatial aliasing (fold-over) of the images. Calculation of the missing data can either be performed as an unfolding process in the image space (SENSE) [3] or directly in the data matrix (k-space) by calculating the missing k-space rows (GRAPPA) in the data matrix [2]. The maximum reduction factor R of undersampling of the data matrix is determined by the number of coil elements for data acquisition.
One problem with implementation is that the use of parallel imaging with increasing reduction factors results both in a clear increase in reconstruction artifacts in the images and in a drastic drop in the signal-to-noise ratio (SNR) in the reconstructed images. For example, with eight coil elements, the maximum reduction factor at which images can still be obtained whose image artifacts and SNR permit their use for diagnosis in clinical applications is approximately 3.
GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisition)
In the reconstruction process of the k-space-related GRAPPA technique, calculation of the missing k-space rows for image reconstruction and combining the coil images reconstructed with it are disconnected. For that reason, reconstruction of the missing data can be optimized separately, allowing robust and optimized parallel image reconstruction.
For calculation of the missing data points of the undersampled data matrix, it is first necessary to determine the spatial sensitivities of the individual coil elements that are described by the ‘coil weighting factors’. This defines a ‘kernel’ that exhibits a certain extent in the spatial direction and comprises both acquired data points (source points) and non-acquired data points (target points). To determine the coil weighting factors and to reconstruct the target points within the kernel, a certain number of source points of the kernel are used.
Parallel Imaging with Temporal and Spatial Information
By including adjacent temporal and spatial information, the quality of parallel imaging reconstruction can be increased, permitting the use of higher reduction factors than in conventional parallel imaging [4-7]. In the TSENSE [4] (SENSitivity Encoding for fast NMR incorporating temporal filtering) and TGRAPPA [5] techniques, data from adjacent measurements are merged to yield the coil weighting factors for the reconstruction process. In kt-SENSE and kt-BLAST [6] (Broad-use Linear Acquisition Speed-up Technique) and in kt-GRAPPA [7], the temporal information contributes directly to the reconstruction process. One disadvantage of the kt-SENSE/kt-BLAST techniques is the restriction to quasi-periodic movement, such as in cardiac imaging. A further disadvantage of the kt-GRAPPA technique is the use of multiple kernels with different geometries for determining the coil weighting factors and the reconstruction, which can result in systematic errors and therefore in image artifacts and can also necessitate long computation times.
For multiple-slice imaging and 3D imaging, methods have been disclosed in which information from spatially adjacent slices was used for reconstruction of missing data rows [8-10]. The acquisition scheme that describes the time sequence of the data matrices differs from that in kt-GRAPPA in that the temporal dimension is replaced by a third spatial direction.